code: def predict(self, X, top_k_list):
      probs = numpy.asarray(super(TopKRanker, self).predict_proba(X))
      all_labels = []
      for i, k in enumerate(top_k_list):
          probs_ = probs[i, :]
          labels = self.classes_[probs_.argsort()[-k:]].tolist()
          probs_[:] = 0
          probs_[labels] = 1
          all_labels.append(probs_)
      return numpy.asarray(all_labels)
  
file_path: OpenNE/src/openne/classify.py
imports: from __future__ import print_function
  import numpy
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.metrics import f1_score
  from sklearn.preprocessing import MultiLabelBinarizer
  from time import time
label_desc: Use image node_id 1 for calling the TopKRanker obj's underlying member method code with example usage: obj.predict(X, top_k_list) and returns: numpy